Independence, successive and conditional likelihood for time series of counts
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Independence, successive and conditional likelihood for time series of counts. / Sørensen, Helle.
In: Journal of Statistical Planning and Inference, Vol. 200, 2019, p. 20-31.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Independence, successive and conditional likelihood for time series of counts
AU - Sørensen, Helle
PY - 2019
Y1 - 2019
N2 - Serial correlation and overdispersion must be handled properly in analyses of time series of counts, and parameter-driven models combine an underlying latent process with a conditional log-linear Poisson model (given the latent process) for that purpose. Regression coefficients have direct interpretations, but likelihood inference is not straight-forward. We consider a two-step procedure for estimation: First regression parameters are estimated from the marginal distribution; second parameters concerning the latent process are estimated with composite likelihood methods, based on low-order simultaneous or conditional distributions. Confidence intervals are computed by bootstrap. Properties of estimators are examined and compared to other methods in three simulation studies, and the methods are applied to two datasets from the literature concerning hospital admission related to asthma and traffic deaths.
AB - Serial correlation and overdispersion must be handled properly in analyses of time series of counts, and parameter-driven models combine an underlying latent process with a conditional log-linear Poisson model (given the latent process) for that purpose. Regression coefficients have direct interpretations, but likelihood inference is not straight-forward. We consider a two-step procedure for estimation: First regression parameters are estimated from the marginal distribution; second parameters concerning the latent process are estimated with composite likelihood methods, based on low-order simultaneous or conditional distributions. Confidence intervals are computed by bootstrap. Properties of estimators are examined and compared to other methods in three simulation studies, and the methods are applied to two datasets from the literature concerning hospital admission related to asthma and traffic deaths.
KW - Bootstrap
KW - Composite likelihood
KW - Generalized linear mixed model
KW - Overdispersion
KW - Serial correlation
UR - http://www.scopus.com/inward/record.url?scp=85054051567&partnerID=8YFLogxK
U2 - 10.1016/j.jspi.2018.09.002
DO - 10.1016/j.jspi.2018.09.002
M3 - Journal article
AN - SCOPUS:85054051567
VL - 200
SP - 20
EP - 31
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
ER -
ID: 203664686